Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces

2025/04/17 by Shimgekar, Soorya Ram, Rodriguez, Violeta J., Bloom, Paul A. +2 · 5 citations
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Social and Information Networks (cs.SI)

paper · doi:10.48550/arxiv.2504.13277

Abstract

Suicide is a critical global public health issue, with millions experiencing suicidal ideation (SI) each year. Online spaces enable individuals to express SI and seek peer support. While prior research has revealed the potential of detecting SI using machine learning and natural language analysis, a key limitation is the lack of a theoretical framework to understand the underlying factors affecting high-risk suicidal intent. To bridge this gap, we adopted the Interpersonal Theory of Suicide (IPTS) as an analytic lens to analyze 59,607 posts from Reddit's r/SuicideWatch, categorizing them into SI dimensions (Loneliness, Lack of Reciprocal Love, Self Hate, and Liability) and risk factors (Thwarted Belongingness, Perceived Burdensomeness, and Acquired Capability of Suicide). We found that high-risk SI posts express planning and attempts, methods and tools, and weaknesses and pain. In addition, we also examined the language of supportive responses through psycholinguistic and content analyses to find that individuals respond differently to different stages of Suicidal Ideation (SI) posts. Finally, we explored the role of AI chatbots in providing effective supportive responses to suicidal ideation posts. We found that although AI improved structural coherence, expert evaluations highlight persistent shortcomings in providing dynamic, personalized, and deeply empathetic support. These findings underscore the need for careful reflection and deeper understanding in both the development and consideration of AI-driven interventions for effective mental health support.

Citations

• Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries
• The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships
• Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use
• Toward Safe Evolution of Artificial Intelligence (AI) based Conversational Agents to Support Adolescent Mental and Sexual Health Knowledge Discovery
• Bias in Generative AI
• Benefits and Harms of Large Language Models in Digital Mental Health
• An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
• Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring
• Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models
• Don't lose the message while paraphrasing: A study on content preserving style transfer
• LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts
• Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders
• BERTopic: Neural topic modeling with a class-based TF-IDF procedure
• The three-step theory of suicide: Description, evidence, and some useful points of clarification
• A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
• "I Cannot Do All of This Alone": Exploring Instrumental and Prayer Support in Online Health Communities
• The Effect of Moderation on Online Mental Health Conversations
• Computer-Mediated Communication, Social Media, and Mental Health: A Conceptual and Empirical Meta-Review
• MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Reflections on Suicidal Ideation
• Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape
• Psychotherapy relationships that work III.
• Social Support, Reciprocity, and Anonymity in Responses to Sexual Abuse Disclosures on Social Media
• Modeling Empathy and Distress in Reaction to News Stories
• Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
• The interpersonal theory of suicide: A systematic review and meta-analysis of a decade of cross-national research.
• Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research.
• Predicting Depression via Social Media
• Mark My Words! Linguistic Style Accommodation in Social Media
• The interpersonal theory of suicide.
• The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
• Suicide and Suicidal Behavior
• A diagnostic meta-analysis of the Patient Health Questionnaire-9 (PHQ-9) algorithm scoring method as a screen for depression
• Research summary on the therapeutic relationship and psychotherapy outcome.

Cited by

• Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
• Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational Theory

Related

• CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation
• Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods
• Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media
• Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
• Enhanced Suicidal Ideation Detection from Social Media Using a CNN-BiLSTM Hybrid Model